Department of Chemistry and Applied Biosciences, Institute for Chemical and Bioengineering, ETH Zurich, Vladimir-Prelog-Weg 1, 8093 Zurich, Switzerland.
Barcelona Supercomputing Center (BSC), Plaça d'Eusebi Güell 1-3, 08034 Barcelona, Spain.
J Am Chem Soc. 2022 May 11;144(18):8018-8029. doi: 10.1021/jacs.1c12466. Epub 2022 Mar 25.
Single-atom catalytic sites may have existed in all supported transition metal catalysts since their first application. Yet, interest in the design of single-atom heterogeneous catalysts (SACs) only really grew when advances in transmission electron microscopy (TEM) permitted direct confirmation of metal site isolation. While atomic-resolution imaging remains a central characterization tool, poor statistical significance, reproducibility, and interoperability limit its scope for deriving robust characteristics about these frontier catalytic materials. Here, we introduce a customized deep-learning method for automated atom detection in image analysis, a rate-limiting step toward high-throughput TEM. Platinum atoms stabilized on a functionalized carbon support with a challenging irregular three-dimensional morphology serve as a practically relevant test system with promising scope in thermo- and electrochemical applications. The model detects over 20,000 atomic positions for the statistical analysis of important properties for establishing structure-performance relations over nanostructured catalysts, like the surface density, proximity, clustering extent, and dispersion uniformity of supported metal species. Good performance obtained on direct application of the model to an iron SAC based on carbon nitride demonstrates its generalizability for single-atom detection on carbon-related materials. The approach establishes a route to integrate artificial intelligence into routine TEM workflows. It accelerates image processing times by orders of magnitude and reduces human bias by providing an uncertainty analysis that is not readily quantifiable in manual atom identification, improving standardization and scalability.
单原子催化位点可能自其首次应用以来就存在于所有负载型过渡金属催化剂中。然而，当透射电子显微镜（TEM）的进步允许直接确认金属位点隔离时，对单原子多相催化剂（SACs）的设计的兴趣才真正增长。虽然原子分辨率成像仍然是一种核心的表征工具，但较差的统计意义、可重复性和互操作性限制了其在这些前沿催化材料中得出稳健特征的范围。在这里，我们引入了一种用于图像分析中自动原子检测的定制深度学习方法，这是实现高通量 TEM 的关键步骤。稳定在功能化碳载体上的铂原子具有具有挑战性的不规则三维形态，是一个具有实际意义的测试系统，在热和电化学应用中具有广阔的前景。该模型检测到超过 20000 个原子位置，用于统计分析建立结构-性能关系的重要特性，例如纳米结构催化剂上负载金属物种的表面密度、临近度、团聚程度和分散均匀性。该模型直接应用于氮化碳负载的铁 SAC 上取得了良好的性能，证明了其在与碳相关的材料上进行单原子检测的通用性。该方法为将人工智能集成到常规 TEM 工作流程中建立了一条途径。它通过数量级加速图像处理时间，并通过提供难以在手动原子识别中量化的不确定性分析，减少人为偏见，从而提高标准化和可扩展性。
